Disaster Recovery Phases: How to Move From Response to Restoration Without Gaps

The phases of disaster recovery are usually simpler than people make them sound.

In practical terms, most organizations move through three core phases: activation and assessment, recovery, and restoration or reconstitution.

In short

Most disaster recovery models can be understood through three practical phases: activation and assessment, recovery, and restoration. The real risk is not forgetting the phase names. It is losing time, ownership, or context in the handoffs between them.

· Activation and assessment determine what happened, what is affected, and what needs to escalate

· Recovery restores critical capability, often through workarounds, alternate processing, or system recovery

· Restoration validates the environment and returns the organization to stable normal operations

That does not mean every organization uses the same labels. Some teams talk about response, recovery, and restoration. Others use activation, stabilization, recovery, and return to normal operations. The terminology varies. The operational challenge does not.

For a practitioner, the hard part is rarely naming the phase. It is knowing what needs to happen in each phase, who owns the handoff, and how to keep the work from stalling between the first response actions and the point where normal operations are actually restored.

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Disaster Recovery Phases: How to Move From Response to Restoration Without Gaps

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How Self-Driving Networks Are Reshaping Healthcare

Autonomous, AI-driven infrastructure is key to next-generation care.

Healthcare delivery now depends on digital systems that operate far beyond the boundaries of traditional IT infrastructure. From clinical communication tools and imaging platforms to remote monitoring and connected devices at the bedside, the network has effectively become part of the care environment itself.

As a result, even minor disruptions in connectivity can have immediate downstream effects on clinical workflows, patient experience, and operational efficiency. This reality places increasing pressure on IT teams to maintain not just uptime, but consistent, intelligent performance across highly dynamic environments.

At the same time, healthcare organizations are contending with expanding device ecosystems, stricter regulatory requirements, and increasingly distributed care models.

These factors are driving levels of complexity that legacy network approaches were never designed to manage effectively.

Against this backdrop, a new operational model is emerging: one that moves network management beyond reactive troubleshooting toward predictive, and ultimately autonomous, operations.

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How Self-Driving Networks Are Reshaping Healthcare

The Time to Set Rules Around AI Use Is Before — Not After — You Deploy It Everywhere

Algorithms can flag suspicious activity, but they can’t yet tell you whether fraud is likely

AI, automation and algorithms are proliferating across many sectors of the global economy, including in regulated industries like pharma, where they are doing things like selecting clinical trial sites. But as regulators catch up, corporate leaders need to get clarity on areas where there can be no substitute for human accountability, says AI governance and board adviser Theodora Monye. 

Across regulated industries, investment in AI is accelerating. The ambitions of AI projects tend to be consistent: faster decisions, reduced operational cost, better outcomes at scale. What is less consistent is where human judgment ends and algorithmic authority begins. That boundary is not a technical question but a governance one. And most organizations are still working on answering it.

The assumption that sufficiently sophisticated algorithms can eventually manage an organization, or significant parts of it, is increasingly embedded in how boards and leadership teams think about AI strategy. It is also wrong, not because algorithms lack capability, but because capability is not the same as accountability. In regulated industries, accountability is not optional.

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 The Time to Set Rules Around AI Use Is Before — Not After — You Deploy It Everywhere

It Is Official That Half of CISOs Pay in Ransomware Attacks

A recent survey conducted by Absolute Security has revealed a concerning trend in the cybersecurity industry: nearly half of Chief Information Security Officers (CISOs) are willing to pay hackers during ransomware attacks in order to restore operations quickly and minimize disruption. The findings highlight the growing pressure faced by cybersecurity leaders when organizations become victims of sophisticated cybercrime.

According to the survey, around 58% of CISOs admitted that they would consider paying ransom demands to recover systems encrypted by ransomware. The willingness to negotiate with cybercriminals appeared higher among respondents in the United States, where nearly 63% supported the idea, compared to 47% in the United Kingdom. Many respondents reportedly believed that paying the ransom could reduce downtime, simplify recovery efforts, and help organizations resume normal operations faster.

However, cybersecurity experts and legal authorities strongly discourage organizations from making such payments. Law enforcement agencies across the world have repeatedly warned that paying hackers not only encourages future cyberattacks but also provides no guarantee that victims will regain access to their data. In several cases, organizations that paid ransom demands either received faulty decryption keys or were targeted again later by the same criminal groups.

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It Is Official That Half of CISOs Pay in Ransomware Attacks

5 Steps for Frontier AI Readiness

As vulnerabilities can be discovered and exploited at faster speeds, organizations must rethink their approach to cyber risk.

Frontier AI models can lower the skill barrier for attackers and compress the time between exposure and exploitation faster than defenders can patch. Learn the five steps organizations must take to prepare as the window between discovery and exploitation closes.

The evolution of frontier AI is reshaping how organizations approach cyber risk. As these highly capable AI models rapidly discover vulnerabilities and develop exploits for them, they are forcing a shift in how businesses evaluate, prioritize, and address areas of exposure.

Frontier AI describes a new class of advanced AI systems that can analyze software, identify vulnerabilities, accelerate exploit development, and support sophisticated security workflows. Anthropic’s Claude Mythos and OpenAI’s GPT-5.4-Cyber are early examples of how AI is expanding offensive and defensive capabilities.

As vulnerabilities can be discovered and exploited at faster speeds, organizations must rethink their approach to cyber risk. For years, security teams operated under an assumption of delays on the adversary’s side. Discovering a vulnerability, turning it into a usable exploit, chaining it into a broader attack, and using it against a target took time and skill. This process created a window, however imperfect, for patching and mitigation.

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5 Steps for Frontier AI Readiness